Non-linearity and temporal changes of fault zone site response associated with strong ground motion

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1 Geophys. J. Int. (9) 17, 7 doi: /j.1-X...x Non-linearity and temporal changes of fault zone site response associated with strong ground motion Chunquan Wu, 1 Zhigang Peng 1 and Yehuda Ben-Zion 1 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA,, USA. chunquanwu@gatech.edu Department of Earth Sciences, University of Southern California, Los Angeles, CA 99-7,USA Accepted October. Received August 7; in original form April 1 SUMMARY We systematically analyse temporal changes of fault zone (FZ) site response along the Karadere-Düzce branch of the North Anatolian fault that ruptured during the 1999 İzmit and Düzce earthquake sequences. The study is based primarily on spectral ratios of strong motion seismic data recorded by a FZ station and a station m away from the fault and augmented by analysis of weak motion records. The observations are used to track non-linear behaviour and temporal changes of the FZ site response. The peak spectral ratio increases 1 per cent and the peak frequency drops per cent at the time of the Düzce main shock. These co-main shock changes are followed by a logarithmic recovery over an apparent timescale of 1 d. However, analysis of temporal changes at each individual station using weak motion waveforms generated by repeating earthquakes show lower-amplitude longer-duration logarithmic recoveries that are not detected by the spectral ratio analysis. The results are consistent with a reduction of S-wave velocities in the top 1 m during the Düzce main shock of per cent or more and logarithmic post-main shock recovery on a timescale of months or more. The observations support previous suggestions that non-linear wave propagation effects and temporal changes of seismic properties are generated in the shallow material by strong ground motion of nearby major earthquakes. Key words: Elasticity and anelasticity; Fault zone rheology; Earthquake ground motions; Site effects; Wave propagation. GJI Seismology 1 INTRODUCTION Large earthquakes occur on major fault zone (FZ) structures. The faulting process produces localized belts of damaged FZ rocks that have lower elastic moduli than the surrounding rocks (e.g. Ben-Zion & Sammis, and references therein). The FZ damage is pronounced in the top few kilometres of the crust, because increasing normal stress suppresses the damage generation and enhances the healing process of damage recovery (e.g. Lyakhovsky et al. 1997; Ben-Zion & Shi ; Johnson & Jia ; Rice et al. ; Finzi et al. ). The top few hundred metres of the crust away from faults are also heavily fractured due to weathering processes, seismic motions, seasonal thermoelastic strain and other forces that operate under very low normal stress conditions. The shallow surface layers are characterized by extremely low shear wave velocity ( m s 1 ) and very high attenuation (Q 1 1; e.g. Aster & Shearer 1991). Quantifying the temporal changes of material properties within and around active FZs and in the near-surface layers is important for better understanding rock rheology and estimating the strong ground motion that can be generated by large earthquakes. As high-amplitude seismic waves propagate through damaged FZ rocks and/or shallow surface layers, they may produce additional damage leading to non-linear wave propagation effects and temporal changes of material properties (e.g. seismic velocity, attenuation). Recent studies based on waveform cross-correlations of repeating earthquakes showed clear changes of seismic velocities in shallow surface layers and around active FZs, with rapid reductions during strong motions of nearby large earthquakes followed by logarithmic recoveries (e.g. Rubinstein & Beroza a, b, ; Peng & Ben- Zion ; Rubinstein et al. 7). The use of repeating earthquakes allows separation of spatial variations from temporal changes of material properties (e.g. Peng & Ben-Zion, ) but is limited by the availability of repeating earthquakes in the study area. Some studies employed repeating artificial sources (e.g. Li et al. 199, ; Vidale & Li ), but these types of studies are limited by high cost and poor depth penetration. Another technique that has been widely applied to study nonlinear site response of surface layers and sediments is the spectral ratio method (e.g. Beresnev & Wen 199; Field et al. 1997; Pavlenko & Irikura ; Sawazaki et al., ). This method generally involves comparisons of weak and strong-motion responses based C The Authors Journal compilation C RAS

2 C. Wu, Z. Peng and Y. Ben-Zion 1 ' ' ' ' (b) km M 7. Ízmit M 7. Istanbul Marmara Sea M Black Sea Adapazari Sapanca Lake M 7. M 7.1 M 7. M BU Akyazi CH NAF 19 M 7. cm/yr Hendek 197 M7. 19 M. GE FP LS FIMO VO WF Almacik Block Gölyaka km 1 ' ' ' ' 1 ' 1 1' 1 ' GE BV Depth range ~ km ~ 1 km 1 ~ 1 km > 1 km Düzce Eften Lake 197 M7. Düzce M 7.1 ' FP A1 ' MO A Karadere VO LS FI ' WF km 1 ' ' ' ' ' Figure 1. Topographic map of the area along the Karadere-Düzce branch of the North Anatolian fault (NAF). Earthquake locations are marked by small dots with colours denoting different depth ranges. Shaded background indicates topography with white being low and dark being high. The surface ruptures of the İzmit and Düzce earthquakes are indicated with blue and black lines, respectively. Dark thin lines associated with earthquake information denote faults that were active during recent ruptures. Other dark thin lines are geologically inferred fault traces. Stations within, near and outside the FZ are shaded with dark, grey and white triangles, respectively. Grey squares denote locations of nearby cities. The area bounded by the red dashed lines is shown in (b). The inset illustrates the tectonic environment in northwestern Turkey with the box corresponding to our study area. Vectors represent plate deformation rate (Reilinger et al. 1997) from GPS data. Modified from Peng & Ben-Zion (). (b) A zoom-in map around the location of the station VO inside the fault and FP m away from the fault. All the symbols and notations are the same as in. The inverted triangles mark seismic stations A1 and A used in the study of Karabulut & Bouchon (7). on the spectral ratio between a target and a reference site (e.g. nearby hard rock site or borehole). Recently, Karabulut & Bouchon (7) applied the spectral ratio method to near-fault ground accelerations to quantify spatial variability and non-linearity along the Karadere- Düzce branch of the North Anatolian fault (NAF) in relation to the 1999 M w 7.1 Düzce earthquake (Fig. 1). Their results indicated per cent reduction in S-wave velocity during the Düzce main shock and near-instantaneous recovery. In comparison, based on C The Authors, GJI, 17, 7 Journal compilation C RAS

3 Non-linear fault zone site response 7 waveform cross-correlations of weak motion generated by repeating earthquakes in the same region, Peng & Ben-Zion () found a much smaller percentage of coseismic reduction in S-wave velocity (up to per cent) and a much longer timescale of recovery (at least months). The differences may stem from different resolutions of the employed data and analysis techniques, along with different site conditions where the instruments are placed. To better quantify temporal changes of FZ properties and investigate the differences in the above two studies, we apply the spectral ratio method to strong and weak motion data recorded by a pair of on- and off-fault stations before and during the M w 7.1 Düzce earthquake and its aftershocks. The analysis of the strong ground motion shows a per cent reduction of peak frequency (frequency of maximum spectral ratio amplitude) and 1 per cent increase of peak spectral ratios (maximum of spectral ratio amplitude) at the FZ station immediately following the Düzce main shock, consistent with the per cent reduction in S-wave velocity found by Karabulut & Bouchon (7). These strong changes of spectral ratios are followed by a logarithmic recovery to the values before the main shock within 1 d. The on-scale weak motion data used in the spectral ratio analysis start only hr after the Düzce main shock and show mild evolution of peak spectral frequency afterwards (within large error bars), consistent generally with the results of Peng & Ben-Zion (). In the following Sections and, we describe the seismic data, analysis procedure and results. In Section, we present synthetic waveform simulations for sets of varying FZ properties that can explain the observed changes. We compare our results with those from previous studies in Section, and discuss possible mechanisms that might be responsible for the observations in Section. DATA AND ANALYSIS PROCEDURE.1 Seismic data The analysis employs primarily strong and weak motion data recorded by two stations VO and FP from a temporary 1-station PASSCAL seismic network (Fig. 1a) along and around the Karadere-Düzce branch of NAF that was deployed a week after the 1999 August 17 M w 7. İzmit earthquake (Seeber et al. ; Ben-Zion et al. ). All 1 stations had REFTEK recorders and -component L short-period sensors with a sampling frequency of 1 Hz. In addition, eight stations (not including MO and GE) had -component force-balance accelerometers (FBAs). During the -month operational period, this network recorded seismograms of about earthquakes, including the 1999 November 1 M w 7.1 Düzce main shock, its foreshocks and aftershocks. We use seismic waveforms recorded by the station pair VO and FP for the main analysis in this work because these stations form the only pair that is deployed at close distance and has both strong and weak motion recordings. In particular, station VO is deployed on a shutter ridge with high relief, composed of gouge and slope debris inside the rupture zone of the İzmit earthquake. Station FP is m off the fault in soil near bedrock with medium relief (Fig. 1b). Additional details regarding the seismic experiment and data set are given by Seeber et al. () and Ben-Zion et al. ().. Analysis procedure The analysis generally follows the procedures of Sawazaki et al. () and Karabulut & Bouchon (7) and is briefly described here. We use the two horizontal-component ground acceleration records at stations VO and FP, which are generated by 1 earthquakes. These include 11 events starting d before the Düzce main shock, the main shock and 19 events within months after the main shock. The local magnitudes of most events range from to, and the hypocentral depths range from to 1 km. The occurrence times, magnitudes and peak ground accelerations (PGAs) associated with the employed events are shown in Fig.. We analyse the entire seismic data set using two slightly different approaches. In the first approach, we use 1-s time windows with origins that are moved forward by s for all waveforms recorded at stations VO and FP. In this case all possible seismic phases, including pre-event noise, P, S and coda waves are analysed together. In the second approach, we only analyse data within a 1 s long coda wave window, which starts from twice the direct S-wave traveltime after the origin time of each event (e.g. Sawazaki et al. ). To avoid mixing coda waves with other phases, we do not use the records in the coda-window-based analysis if there are any other events between the onset of the S-wave and the end of the coda window or if S-wave phases cannot be clearly identified. Next, we remove the mean value of the traces and apply a per cent Hanning taper to both ends. We add the power spectra of the two horizontal components and take the square root of the sum to get the amplitude of the vector sum of the two horizontal spectra. The obtained spectra are smoothed by applying the mean smoothing algorithm from the subroutine smooth in the Seismic Analysis Code (Goldstein et al. ), with half width of five points. The spectral ratio is obtained by taking the ratio of the horizontal spectra for stations VO and FP. Fig. illustrates the analysis procedure with example waveforms generated by the Düzce main shock. The same procedure is applied to the weak motion data. We do not analyse time windows in which the peak velocity is above per cent of the maximum observed level at a given station, since these windows may be associated with clipped portions of waveforms (Yang et al. 7). RESULTS.1 Strong motion data After processing the sliding-window-based data, we obtain from the first approach 7 spectral ratio traces, including 9 before the Düzce main shock, 7 during the main shock and 717 after the main shock. From the coda-window-based analysis, we obtain 97 spectral ratio traces, including before the main shock and after the main shock. Fig. shows a comparison of spectral ratios for the direct S-wave and the coda waves for 1 events within hr after the main shock. The spectral ratios calculated from coda waves are generally similar to those calculated from the direct S-waves but with less scatter. This is consistent with the observations by Sawazaki et al. () and Mayeda et al. (7). This phenomenon can be attributed to the random distribution of heterogeneities and backscattered waves in the coda (Aki 199; Aki & Chouet 197). Such random distribution provides an averaging effect for the observed spectral ratios by making them less dependent on propagation paths than the direct S-wave (Phillips & Aki 19). The disadvantage of using only the coda waves is that data in a certain time range (e.g. immediately after the main shock) may not be used due to overlapping seismic records (e.g. Peng et al. ). We therefore analyse in this study results using both approaches. C The Authors, GJI, 17, 7 Journal compilation C RAS

4 C. Wu, Z. Peng and Y. Ben-Zion PGA (g) Days after Düzce mainshock (b) Magnitude Days after Düzce mainshock Figure. Peak Ground Acceleration (PGA) values of the earthquakes used in this study plotted against occurrence times (days in linear scale before and logarithmic scale after the Düzce main shock). (b) Magnitudes of the earthquakes used in this study, plotted against occurrence times (same as in a). Open and filled circles mark events that have magnitudes and those that do not, respectively. The events with no magnitudes are plotted at the bottom of the figure around magnitude of 1, with a random number added for plotting purposes. The temporal changes of spectral ratio for all phases and coda wave windows are shown in Figs and (b), respectively. In both plots, we observe a clear increase in peak spectral ratios and reduction in peak frequencies at the time of the main shock, followed by logarithmic recovery. To better quantify the degree of coseismic changes and the timescale of postseismic recovery, we obtain the values of peak spectral ratios and peak frequencies for all traces and average them in different time periods. We divide the whole data set into the following periods: before the main shock, 1 s after the main shock, 1 s after the main shock and then every C The Authors, GJI, 17, 7 Journal compilation C RAS

5 Non-linear fault zone site response 9 Acceleration (g) Acceleration Spectra (g/hz).. VO FP. 1 1 Time (s).1 (b) (c). VO FP 1 1 Spectral Ratio Figure. Ground accelerations recorded at the two stations VO and FP during the 1999 Mw7.1Düzce, Turkey, earthquake. The red dashed lines indicate the time window that is used to compute the spectra in (b) and spectral ratio in (c) coda (b) 7 7 Spectral Ratio Figure. Spectral ratios for the strong motion data from direct S waves and (b) coda waves for 1 events occurred within hr after the Düzce main shock. The black solid and dashed lines show the average spectral ratios and the standard deviation.. interval in logarithmic timescale after 1 s. We have tested averaging the peaks using different time windows and the obtained results remain essentially the same. We use logarithmic, instead of linear time because previous studies have found a logarithmic healing process for long-term recovery (e.g. Rubinstein & Beroza ; Schaff & Beroza ; Peng & Ben-Zion ; Sawazaki et al. ). Next, we compute the average of the peak spectral ratios and peak frequencies in each time period. The same procedure is C The Authors, GJI, 17, 7 Journal compilation C RAS

6 7 C. Wu, Z. Peng and Y. Ben-Zion Figure. Temporal changes of sliding-window-based spectral ratios for the strong motion data at stations VO and FP. The right bin shows the colour-coded spectral ratios for the main shock and aftershocks. The left bin shows those before the main shock. Values shown on the top and bottom of the figure indicate lapse times after the main shock in seconds and in days, respectively. White colour represents no data. The horizontal and inclined black dashed lines show the pre-main shock value and the recovery trend, respectively. (b) Temporal changes of coda-window-based spectral ratio for the strong motion data at stations VO and FP. Symbols and notations are the same as in. applied to the coda-window-based spectral ratio traces. Due to lack of data and overlapping records, there are gaps in the coda-window analysis at and 1 s after the main shock. The averaged peak spectral ratios and frequencies with their standard deviations based on the analysis with all phases and only coda windows are shown in Figs and (c) and 7 and (c), respectively. For comparison, the averaged peak spectral ratios and frequency in the period s after the main shock from all phases are included in the results of the coda-window-based analysis (Figs 7a and c). The results from both data sets show a sudden increase of peak C The Authors, GJI, 17, 7 Journal compilation C RAS

7 Non-linear fault zone site response 71 Spectral Ratio Strong Motion Strong Motion (c) Time since Duzce mainshock (day) (b) Weak Motion Weak Motion (d) (day) Time since Duzce mainshock Figure. Averaged peak frequencies from the sliding-window-based strong-motion data before (red), during (green) and after (blue) the main shock, recorded at stations VO and FP. Vertical solid bar centred at each data point shows the standard deviation. The red dashed line indicates the pre-main shock value of peak frequency. The vertical black line with arrow marks the time of 1 d after the main shock. Values shown on the top and bottom of the figure indicate lapse times after the main shock in seconds and in days, respectively. (b) Same plot as for the weak motion data. (c) Averaged peak spectral ratios with the same symbols and notations as. (d) Same plot as (c) for the weak motion data. Spectral Ratio Strong Motion Strong Motion (c) Time since Duzce mainshock (s) (b) Weak Motion Weak Motion (d) (day) Time since Duzce mainshock Figure 7. Averaged peak frequencies from the coda-window-based strong-motion data before (red), during (green) and after (blue) the main shock, recorded at stations VO and FP. (b) Same plot as for the weak motion data. (c) Averaged peak spectral ratios with the same symbols and notations as. (d) Same plot as (c) for the weak motion data. Other symbols and notations are the same as in Fig.. spectral ratio and a shift of the spectral peak to lower frequencies during the main shock. The values of the averaged peak spectral ratios and peak frequencies, together with the increase/reduction or recovery rates before, immediately after and 1 d after the main shock are listed in Table 1. Both types of spectral ratio change show a near-complete recovery with a timescale of 1 d after the main shock. We have performed the same analyses on the vertical component seismograms but the results did not show clear spectral peaks and temporal changes in the same frequency range. This is consistent C The Authors, GJI, 17, 7 Journal compilation C RAS

8 7 C. Wu, Z. Peng and Y. Ben-Zion Table 1. Averaged peak spectral ratios, peak frequencies and increase/recovery rates with error assessments before, immediately after and 1 d after the main shock from the sliding-window based analysis and (b) the coda-window based analysis. Time period Peak spectral ratio Increase/Recovery rate (per cent) Peak frequency (Hz) Reduction/Recovery rate (per cent) The sliding-window-based analysis Before the main shock. (±.). (±.9) Main shock s. (±1.) 9 (±1) increase. (±.1) (±) reduction One day after the main shock. (±.) 9 (±) Recovery. (±.) 1 (±7) recovery (b) The coda-window-based analysis Before the main shock.7 (±1.). (±.) Main shock s. (±1.) 1 (±7) increase. (±.1) (±) reduction One day after the main shock. (±1.) 1 (±) recovery. (±.) 1 (±) recovery with observations that horizontal components are generally more sensitive to site amplification and other local structures than vertical components (e.g. Castro et al. 1997).. Weak motion data Figs (b) and (d) and 7(b) and (d) show corresponding results for the weak motion data by applying the same averaging technique that has been used for the strong motion data. The obtained peak frequencies and spectral ratios are very similar to those from the strong motion data. Unfortunately, the results from the weak motion data lack useful data points within the first few hours after the main shock due to severe clipping of waveforms on the weak motion instruments. The peak frequencies from the time period with useful weak motion data are increasing gradually with time in the months following the Düzce main shock. However, the large error bars imply that these changes are not well resolved by the employed spectral ratio technique. The amplitudes of the spectral ratios of the weak motion data do not show a clear pattern. In the following sections we focus on the results obtained from the strong motion data. SYNTHESIS WAVEFORMS In this section, we attempt to relate the observed temporal changes of peak frequency and spectral ratios to changes of elastic properties within the FZ. Assuming a low-velocity FZ layer of width W in a half-space (HS), the relation between the FZ fundamental resonance frequency f and the S-wave velocity β FZ inside the FZ is given by f = β FZ /W. A shift of peak frequency to lower value during the strong ground motions of the Düzce main shock can hence be related to the coseismic reduction of the S-wave velocity inside the FZ (Karabulut & Bouchon 7). To quantify the relationship between the observed spectral changes and the velocity reduction in the FZ, we use the -D analytical solution of Ben-Zion & Aki (199) and Ben-Zion (199) for waveforms generated by a shear dislocation in a structure with a low-velocity FZ layer (Fig. ). Since the velocity variations along the FZ are much smaller than those across it, the -D model should provide a valid simplification of the -D velocity variations in the vicinity of the FZ. We simulate ground motion inside and outside the FZ, using parameters obtained from analysis of FZ trapped waves in thesameregion(ben-zionet al. ). The employed parameters are: FZ width W = 1 m, S-wave velocities in the FZ and the Figure. A three-media model for a uniform low-velocity FZ structure in a half-space. The source is an SH line dislocation with coordinates (x S,z S ). The width, shear attenuation coefficient and shear wave velocity of the FZ are marked by W,Q FZ and β FZ, respectively. The shear wave velocity and the attenuation coefficient of the HS are denoted by β HS and Q HS, respectively (after Ben-Zion et al. ). C The Authors, GJI, 17, 7 Journal compilation C RAS

9 Non-linear fault zone site response 7 Normalized Amplitude Spectra VO FP 1 Time (s) VO FP (b) Spectral Ratio 1 (c) Figure 9. Synthetic ground acceleration waveforms from the model in Fig.. The acceleration traces are filtered between 1 and 1 Hz. The spectra in (b) and spectral ratio in (c) are smoothed by applying a mean smoothing algorithm with half width of 1 point. HS β FZ = 1. km s 1 and β HS =. km s 1, and quality factors in the FZ and the HS Q FZ = 1 and Q HS = 1. As mentioned, Ben-Zion et al. () and Peng & Ben-Zion () found that the FZ damaged layer in the study area is confined primarily to the top km. We thus put the shear dislocation source at the depth of km inside the FZ and 9 m from the left-hand boundary near the right-hand FZ interface. Station VO is placed in the middle of the FZ at the free surface and station FP is placed mawayfrom VO. We use the first s of the calculated synthetic ground velocity records, transfer them into acceleration records and then compute the spectral ratio. An example is shown in Fig. 9 to illustrate the procedure. To estimate the expected spectral changes associated with evolution of FZ properties, we compute spectral ratios from sets of synthetic seismograms generated by changing the seismic velocity in the FZ and keeping the other parameters the same as above. Fig. 1 shows spectral ratios obtained with five different values of β FZ.Astheβ FZ valueisreducedfromto1kms 1, the peak frequency decreases from. to. Hz and the peak spectral ratio increases from.9 to 7.. These changes are consistent with the general patterns observed in Figs and. We note that for a FZ width of 1 m, the peak frequency predicted from the basic relation f = β FZ /W is 1 Hz for β FZ = kms 1,andHzforβ FZ = 1kms 1. This value is higher than the simulation results because the synthetic calculations employ a very low quality factor Q FZ = 1 inside the FZ. The associated high attenuation tends to shift the fundamental frequencies of FZ waves to lower values (e.g. Ben- Zion 199). If we use the same quality factor of 1 for both the FZ and HS, the obtained peak frequencies match the basic equation well. To characterize better the likely changes of the FZ S-wave velocity, we perform synthetic simulations with β HS values in the range from.tokms 1 with increments of.1 km s 1 and search for the values that best fit the observed peak frequency shifts (Fig. a). The best fitting velocities and the corresponding peak frequencies and peak spectral ratios are shown in Figs 11 (c). The synthetic results indicate a reduction of the β FZ value from 1. km s 1 before the main shock to.97 km s 1 immediately after the main shock ( per cent change) and near-complete recovery in 1 d. We also perform a grid search of the FZ quality factor with the fixed β FZ value of 1. km s 1 (Figs 11d f). The results show that reducing the Q FZ value can explain the observed peak frequency shift. However, in this case the peak spectral ratios decrease, which is in contrast to our observations. The simulations indicate that temporal changes of β FZ values play a major part in causing the observed spectral changes. A more complete analysis will employ simultaneous changes of multiple parameters (e.g. β FZ, Q FZ and W values), but this is left for a future work. COMPARISONS WITH PREVIOUS STUDIES The Karadere segment of the NAF has been the focus of several detailed studies on spatio-temporal changes of FZ properties. Ben- Zion et al. () performed a comprehensive analysis of seismic FZ trapped waves using the data set described in Section.1. They concluded that the trapped waves along the Karadere segment are generated by 1 m wide FZ layers that extend primarily to km depth and are characterized by strong attenuation (Q values of 1 1) and per cent velocity reduction from that of the host rock. Systematic analyses of seismic anisotropy with fault-parallel cracks in the same area (Peng & Ben-Zion, ) indicated that the trapping structures are surrounded by 1 km wide zones with lower damage, which also extend primarily to the top few kilometres of the crust. Peng & Ben-Zion () and Karabulut & Bouchon (7) observed clear temporal changes of seismic C The Authors, GJI, 17, 7 Journal compilation C RAS

10 7 C. Wu, Z. Peng and Y. Ben-Zion β =1.km/s FZ β =1.km/s FZ 7. (b) 7 β =1.km/s FZ β =1.km/s FZ β =.km/s FZ Peak Spectral Ratio 7. Horizontal Spectral Ratio Peak (c) Figure 1. Synthetic spectral ratios between stations VO and FP for five FZ S-wave velocities marked on the top right corner. (b) Peak spectral ratio and (c) peak frequencies plotted against FZ S-wave velocity for the traces shown in. properties and non-linear wave propagation effects near the Karadere fault. The damaged FZ layers that produce trapped waves, seismic anisotropy, scattering, and non-linear wave propagation effects are all likely parts of a hierarchical flower structure that resides primarily above the seismically-active portion of the crust. The anomalous wave propagation effects of the shallow damaged FZ layers may be referred to collectively as FZ-related site effects (Ben-Zion et al. ). Peng & Ben-Zion () used a moving-window waveform crosscorrelation technique to monitor the temporal changes of seismic velocities after the Düzce main shock, using weak motion generated by repeating earthquakes. They found clear step-like co-main shock changes followed by logarithmic recovery over a timescale of at least months in direct S and early S coda waves (Fig. 1a). The spectral ratio analysis of the strong ground motion in this study indicates per cent reduction of the peak frequency and recovery with timescale of 1 d. The spectral ratio analysis of weak motion in this study shows a mild recovery over the months duration of the data within large scatter. Assuming that the observed temporal changes are accumulated in the top km of the crust (Ben-Zion et al. ), the coseismic change of seismic velocities extrapolated to the main shock time is on the order of a few percent. However, if we assume that the thickness of the layer responsible for the temporal changes in only m (e.g. Rubinstein & Beroza ), the inferred coseismic velocity change at the FZ station VO is about 17 per cent (Fig. 1b). This value is much closer to the per cent changes observed by Karabulut & Bouchon (7) and in this study. Further decreasing the layer thickness to smaller values like 1 m would result in a better match of the coseismic changes. As summarized in Fig. 1, our spectral ratio results based on the strong motion data indicate a recovery timescale of 1 d, which is much shorter than the month timescale of Peng & Ben- Zion () but significantly longer than the near-instantaneous recovery inferred by Karabulut & Bouchon (7). One possible explanation for the different results is the differences in the timescale of the usable data. The coseismic change and the apparent recovery within 1 d observed in this study are obtained from on-scale strong motion recordings during and immediately after the main shock (Figs 7). As noted in Section, most of the weak motion recordings are saturated in this time period, thus preventing similar analysis based on repeating earthquakes (e.g. Peng & Ben-Zion ). Some waveforms may be clipped or buried inside seismograms of other events immediately after the main shock, and many earthquakes (including repeating events) are not detected C The Authors, GJI, 17, 7 Journal compilation C RAS

11 Non-linear fault zone site response (s) (s) (d) 1. 1 FZ Quality Factor 1 1. (b) (e) Peak Peak Peak Spectral Ratio (day) Time since Duzce mainshock (c) Peak Spectral Ratio (day) Time since Duzce mainshock (f) Figure 11. Best-fitting S-wave velocities from waveform simulation inside the FZ before (red), during (green) and after (blue) the main shock to the observed frequency shifts in Fig.. The red dashed line indicates the pre-main shock value. The vertical black arrow marks the time of 1 d after the main shock. Values shown on the top and bottom of the figure indicate lapse times after the main shock in seconds and in days, respectively. (b) Synthetic peak frequencies and (c) synthetic peak spectral ratios by varying the FZ S-wave velocity, with the same symbols and notations as. (d) Best-fittinsg quality factor from waveform simulation. (e) Synthetic peak frequencies and (f) synthetic peak spectral ratios by varying FZ Q value. in the standard earthquake catalogues (e.g. Peng et al., 7; Enescu et al. 7). A systematic search for repeating earthquakes in the early aftershock period (e.g. Peng & Zhao ) could provide useful information on the coseismic changes in material properties of the shallow crust and how they evolve within the initial minutes to hours after the main shock. Another basic explanation for the differences of results is the use of different analysis techniques. In this study, we employ station FP as the reference site for the spectral ratio calculations. However, as shown by Peng & Ben-Zion () and Fig. 1, changes of seismic velocities are observed at both stations VO and FP during the Düzce main shock, although the degree of change at FP is smaller than that at VO. It is therefore clear that calculations based on the spectral ratios between these two stations give lower bound values for both the amplitude of the co-main shock change at station VO and the timescale of recovery. The spectral ratios of the strong motion records provide relatively high-resolution results on the very early large temporal changes of FZ properties following the Düzce main shock. The lower-amplitude longer-duration variations that occur at both stations are generally beyond the sensitivity of the spectral ratio analysis, as evident by the relatively constant values and large error bars for both the strong and weak motion data. Such subtle changes could be measured better by the repeating earthquakes analysis (e.g. Peng & Ben-Zion ). The difference in the timescale of recovery between our results and those from Karabulut & Bouchon (7) probably also stems from differences in the data sets and analysis procedures. In this study, we analysed both weak and strong motion recordings from several days before to 7 d after the Düzce main shock. In addition, the distance between the station pair employed in this study is m, smaller than the 1. km distance for the station pair used by Karabulut & Bouchon (7). Since we analysed a larger data set having smaller path effects, we are probably able to observe finer details of the very-early strong co-main shock changes and the more subtle longer-duration recovery process. DISCUSSION Many factors could contribute to the temporal changes of seismic spectra observed in this study. These include apparent changes C The Authors, GJI, 17, 7 Journal compilation C RAS

12 7 C. Wu, Z. Peng and Y. Ben-Zion (s). VO, clusters, 7 events FP, clusters, events km (day) (s) (b) window of 1 km m 1 m Karabulut & Bouchon (7) This study () (day) Time since Duzce mainshock Figure 1. Relative S-wave velocity changes inferred from time delays for early S-coda waves by Peng & Ben-Zion (). The blue crosses and red circles show measurements for stations VO and FP, respectively. The horizontal black solid line indicates the pre-main shock level of per cent. The blue and red dashed lines are least-squares fits to the data, and the red arrow indicates the recovery time of at least months. Values shown at the top and bottom ofthe figure indicate lapse times after the Düzce main shock in seconds and in days, respectively. (b) Relative S-wave velocity changes measured from the averaged peak frequencies for the coda-window based analysis. Vertical solid bar centred at each data point shows the standard deviation. The black dashed line shows the least-squares fit to the data observed in this study. The blue (VO) and red (FP) dashed lines, dash-dot lines and dotted lines show the least-squares fits to the data shown in assuming that temporal changes occur in the top km, m and 1 m, respectively. The green, black and red arrows mark the time scale of recovery of 1 s inferred by Karabulut & Bouchon (7), 1 d in this study and at least months by Peng & Ben-Zion (), respectively. produced by varying radiation from sources at different locations, and physical changes occurring in shallow sediments and within the damaged FZ rocks. Because the two employed stations are very close to each other ( m) compared with the distances between the earthquakes sources and stations (typically more than 1 km), apparent changes from variable source locations are unlikely to be the reason for the observed changes. The presence of hard rock outcrops near the two stations VO and FP (Herece & Akay ) and the location of station VO within the FZ, reduce the likelihood that the results are dominated by shallow sediments, although such changes can contribute to the observations. Temporal changes of seismic properties within the damaged FZ rocks that are initiated by the strong ground motion of the Düzce main shock are likely to be the main origin of the observed variations of the spectral ratios and peak frequencies. As suggested by the synthetic waveform simulations, the abrupt co-main shock changes are likely associated with reduction of seismic velocity in the FZ layer and to a lesser extent probably also reduction of the FZ attenuation coefficient. Such changes involve physically increasing crack and void densities in the shallow FZ structure, which can be generated by opening and growth of cracks inside the FZ and possibly also reduced packing of the near-surface granular material. During the post-main shock period, the normal stress produces reversed processes associated with decreasing density of crack surfaces and increasing sediment packing. These healing processes produce logarithmic recovery of properties that approach asymptotically the C The Authors, GJI, 17, 7 Journal compilation C RAS

13 Non-linear fault zone site response 77 pre-main shock levels. The discussed effects are observed in laboratory experiments with granular material and rocks (e.g. Dieterich & Kilgore 199; Scholz ; Johnson & Jia ) and may be simulated using damage rheology models (e.g. Hamiel et al. ; Finzi et al. ; Lyakhovsky & Ben-Zion ). The opening and growth of cracks in the damaged FZ material during strong ground motion is expected to increase the contrast of the seismic velocity across the FZ. This can increase the amplification of ground motion within the FZ, although part of the seismic energy could be consumed in the crack opening and growth processes and the amplification may be reduced partially by increasing attenuation. It is important to try to separate effects associated with reduced seismic velocities and increasing attenuation. However, detailed investigation of temporal changes in attenuation of the FZ material requires nearly identical microseismic sources (e.g. Bokelmann & Harjes ; Chun et al. ) to preclude source, path and other variations, which is beyond the scope of this study. It is not clear what parameters control the timescale of recovery. Previous studies suggested that diffusion of fluids may play an important role in the coseismic reduction and postseismic logarithmic recovery (e.g. Schaff & Beroza ; Sawazaki et al. ). However, logarithmic recovery is also expected due to generic enlargement of the real contact area of crack surfaces and grain contacts under normal stress (e.g. Marone 199; Johnson & Sutin ). Although there is no direct evidence for the presence of fluids in our study area, many studies have observed widespread increase of water flow after several major earthquakes (e.g. Rojstaczer et al. 199; Brodsky et al. ; Wang et al. ; Manga & Wang 7). They proposed that increasing permeability in the near-surface layers due to strong shaking from the main shock is the main cause of increased fluid flow. Sawazaki et al. () found a 7 per cent reduction of the peak frequencies in the coda-window-based spectral ratios using data recorded by vertical borehole arrays during the Western Tottori and Tokachi-Oki Earthquake sequences in Japan. The coseismic reduction is followed by a logarithmic recovery to pre-main shock value with timescales ranging from a few tens of minutes at a soft sandy gravel site to a few years at solid and weathered hard rock sites. If fluids play an important role in the process, the observed timescale could be mainly controlled by the permeability at each site, which is related to the specific site conditions, and would increase with the amplitude of seismic waves (Elkhoury et al. ). Additional field and laboratory studies are needed to clarify the precise processes controlling the coseismic changes and timescale of the recovery process. Our observations and the results of Karabulut & Bouchon (7) of large coseismic reduction of seismic velocities at FZ sites support the notion (e.g. Beresnev & Wen 199; Field et al. 1997) that non-linearity is widespread in the shallow crust during strong ground motion of moderate to large earthquakes. The analysis done in this study provides a bridge between the large-amplitude nearinstantaneous changes inferred by Karabulut & Bouchon (7) and the lower-amplitude longer-duration variations inferred by Peng & Ben-Zion (). The changes observed by the spectral ratio analysis provide a refinement for the beginning of the longer more gradual process observed by Peng & Ben-Zion () and other related works based on waveform analysis of repeating earthquakes (e.g. Rubinstein & Beroza a, b, ; Rubinstein et al. 7). 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